Getting started with Vision

This section describes how to get started with the Vision API for image analysis.

Before getting started

To use the examples, install cURL and get the authorization data for your account:

Analyze the image

You can use any of the analysis features from the list. For example, use the Text recognition feature:

1. Prepare an image file that meets the requirements:

   • Supported file formats: JPEG, PNG, PDF.

     You specify the MIME type of the file in the mime_type property. The default is image.

   • Maximum file size: 1 MB.

   • Image size should not exceed 20 MP (length x width).

   Note

   Need a sample image? Download an image of the penguin crossing road sign.

2. Encode the file as Base64:

   UNIX

   Windows

   PowerShell

   Python

   Node.js

   Java

   Go

   $ base64 -i input.jpg > output.txt
   

   C:> Base64.exe -e input.jpg > output.txt
   

   [Convert]::ToBase64String([IO.File]::ReadAllBytes("./input.jpg")) > output.txt
   

   # Импортируйте библиотеку для кодирования в Base64
   import base64
   
   # Создайте функцию, которая кодирует файл и возвращает результат.
   def encode_file(file):
     file_content = file.read()
     return base64.b64encode(file_content)
   

   // Считайте содержимое файла в память.
   var fs = require('fs');
   var file = fs.readFileSync('/path/to/file');
   
   // Получите содержимое файла в формате Base64.
   var encoded = Buffer.from(file).toString('base64');
   

   // Импортируйте библиотеку для кодирования в Base64.
   import org.apache.commons.codec.binary.Base64;
   
   // Получите содержимое файла в формате Base64.
   byte[] fileData = Base64.encodeBase64(yourFile.getBytes());
   

   import (
       "bufio"
       "encoding/base64"
       "io/ioutil"
       "os"
   )
   
   // Откройте файл
   f, _ := os.Open("/path/to/file")
   
   // Прочитайте содержимое файла.
   reader := bufio.NewReader(f)
   content, _ := ioutil.ReadAll(reader)
   
   // Получите содержимое файла в формате Base64.
   base64.StdEncoding.EncodeToString(content)
   

3. Create a file with the request body (for example, body.json). In the content property, specify the Base64-encoded image.

   For the service to automatically detect the text language, specify the "language_codes": ["*"] property in the configuration.

   body.json:

   {
       "folderId": "b1gvmob95yysaplct532",
       "analyze_specs": [{
           "content": "iVBORw0KGgo...",
           "features": [{
               "type": "TEXT_DETECTION",
               "text_detection_config": {
                   "language_codes": ["*"]
               }
           }]
       }]
   }
   

4. Send a request using the batchAnalyze method and save the response in a file, such as output.json:

   $ export IAM_TOKEN=CggaATEVAgA...
   $ curl -X POST \
       -H "Content-Type: application/json" \
       -H "Authorization: Bearer ${IAM_TOKEN}" \
       -d '@body.json' \
       https://vision.api.cloud.yandex.net/vision/v1/batchAnalyze > output.json
   

   The response will consist of recognized blocks of text, lines, and words with their position on the image indicated:

   {
     "results": [{
       "results": [{
         "textDetection": {
           "pages": [{
             "blocks": [{
               "boundingBox": { ... },
               "lines": [{
                   "boundingBox": { ... },
                   "words": [{
                     "boundingBox": {
                       "vertices": [{
                           "x": "504",
                           "y": "760"
                         },
                         {
                           "x": "504",
                           "y": "836"
                         },
                         {
                           "x": "826",
                           "y": "836"
                         },
                         {
                           "x": "826",
                           "y": "760"
                         }
                       ]
                     },
                     "languages": [{
                       "languageCode": "en",
                       "confidence": 0.9520227313
                     }],
                     "text": "PENGUINS",
                     "confidence": 0.9520227313
                   }],
                   "confidence": 0.9520227313
                 },
                 ...
               ]
             }],
             "width": "1920",
             "height": "1280"
           }]
         }
       }]
     }]
   }
   

5. To get all the recognized words from the image, find all the lines with the text property (one way to do this is using the grep utility):

   $ grep -o "\"text\":\s\".*\"" output.json
   "text": "PENGUINS"
   "text": "CROSSING"
   "text": "SLOW"
   

What's next

• Find out about all the service features
• Learn about API authentication methods
• Detect faces in a photo
• Assess the quality of an image
• Moderate images